Computer Vision

Course topics

Part 1. Correspondence of Local Features

• Local features: introduction, terminology
• Motivation: a generalization of local stereo to wide-baseline stereo
• Detection of Local invariant features
• Descriptors, Matching
• Correspondence Verification
• RANSAC (robust model fitting)

Part 2. Tracking

• Template Matching and the Lucas-Kanade Method
• Mean Shift HW

Part 3. Graphical Models

• Probabilistic models. Decisions under uncertainty
• Hidden Markov Model. Markov chain, different factorizations. MAP problem — Viterbi algorithm
• Marginals problem — forward-backward algorithm
• Generalized dynamic programming
• Markov Random Field. MAP in MRF
• Energy minimization, solvable classes, graph cuts, relaxations
• Graphical models as neural networks. Sigmoid Belief network
• Uncertainties, noises
• Variance propagation methods
• Bayesian Learning. Variational Bayesian learning

Part 4. Geometry

• Perspective camera model and calibration
• Homography between two images
• Projection and homography
• Epipolar geometry and camera motion
• 3D Reconstruction
• Epipolar geometry and 3D reconstruction

Part 5. Retrieval

• Image Retrieval
• K-Means
• Min-hash

Part 6. Deep Learning for Computer Vision

Part 7. Advanced CNN Topologies

Prerequisites

• Linear Algebra for Data Science
• Statistics and Econometrics
• Deep Learning